Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Liver Cancer Prediction Using Machine Learning: Enhancing Early Detection and Survival Analysis
No full textLiver cancer is still one of the most lethal cancers in the world, with consistently increasing rates in the United States that are caused by rising rates of obesity, rates of hepatitis infection, and liver disease that is associated with alcohol. Early detection of liver cancer is crucial for improving patient survival because liver cancer is typically found in advanced stages with dismal survival rates and few treatment choices. The overall objective of this study was to create and test machine-learning models for liver cancer diagnosis and survival prediction. The research focused on machine learning in the U.S. health system using patient data with different demographic and clinical backgrounds. The dataset for this study is a rich patient dataset collected with great care to support machine learning model development for liver cancer detection and survival prediction. It had detailed patient demographic data, including age, gender, ethnicity, and geographic origin, that are crucial for population-based risk factor identification and liver cancer disparities. Additionally, the dataset has large medical history records of pre-existing conditions of chronic infections with hepatitis B and C, cirrhosis, NAFLD, diabetes, and alcohol use disorder that are crucial liver cancer risk factors. Genetic factors like SNPs and gene expression patterns that are implicated in liver cancer are also present to study genetic susceptibility to disease development and progression. Clinical test results like ultrasounds, CT and MRI images, and biomarker levels like AFP and DCP form a robust platform for diagnostic and prediction modeling. The dataset is obtained from multiple high-quality sources like Electronic Health Records (EHRs) of top health centers, anonymized patient databases of hospitals, and national cancer databases like the Surveillance, Epidemiology, and End Results (SEER) Program. In addressing the dual objectives of liver cancer detection and survival prediction, a combination of machine learning models was employed, with each chosen for its specific strength. Accuracy, precision, recall, and F1-score were used for classification tasks to test whether liver cancer was identified by the models. XG-Boost performs better than both models with the highest accuracy and with strong precision, recall, and F1 scores, representing its strength in classification. The use of AI tools in the U.S. health system can revolutionize methods of early detection for liver cancer and address one of oncology\u27s biggest challenges. With machine learning models that are trained on rich databases, clinicians can be equipped with potent diagnostic tools that enhance their ability to diagnose liver cancer in its earliest and most curable stages. The use of machine learning models in clinical decision support systems (CDSS) is a revolutionary opportunity to improve liver cancer treatment in the U.S. health system. The application of AI-based predictive models in liver cancer treatment has important public health and policy implications for the United States.
اللاميتان: جدلية الثورة والبيئة بين الشنفرى والطغرائي
No full textهدفت هذه الدراسة إلى تحليل لاميتي العرب للشنفرى والعجم للطغرائي بغية الوصول إلى إطار مشترك يوطد الصلة بين متشابهات القصائد من ناحية الروي والقافية والتسمية. استخدمت الدراسة المنهج الوصفي التحليلي الذي يقوم بوصف الظاهرة وتحليلها بغية الوصول إلى نتائج مهمة.خرجت الدراسة بعدة نتائج أهمها: كلا الشاعرين عاش حياة فقر وعوز وضنك وقد استطاع أن يبرز ذلك في الفاظ جميلة حملت معاني ممتلئة بالدقة والرقي. مال الشنفرى إلى ألفاظ متعددة أملتها عليه طبيعة الظرف الذي كان يعيشه، وكذلك أن معانيه كانت ملائمة للظرف الذي هو فيه، لذلك نجده قد استخدم ألفاظاً بدوية أعرابية وأخرى حوشية غريبة، أما الطغرائي فنجده قد استخدم أيضاً ألفاظاً تناسبه والظروف التي عاشها وأن معانيه جاءت متوافقة، فاستخدم ألفاظاً حضارية، وأخرى مدنية وثالثة بدوية أعرابية، وهذا كله أملته عليهما طبيعة الحياة التي كانوا يعيشانها. إن الشنفرى قد أعلن ثورة صريحة على مجتمعه فثار بثورته على التسلط والتجبّر والاحتقار، وكانت ثورته عمل وجد وقوّة وعنف، وثورة الطغرائي كانت ثورة لفظية عاطفية تكفي بذم الدهر وفساد الحكّام وجور الحظوظ. أن كلاً منهما قد نظمت على بحر، فالأولى ((لامية الشنفرى)) نظمت على بحر الطويل، والثانية ((لامية العجم)) نظمت على البسيط، وشتان بين الطويل والبسيط في التفعيلات والمعاني والدلالة، فهذا يدل على طول النفس وذاك على السرعة والانتقال. أوصت الدراسة بتتبع مشرب ثقافة الطغرائي بصورة أدق تمكن من سبر أغوار لاميته الرائعة
Transnationality, Mobile Identity, and Cultural Dislocation in Rabih Alameddine’s I, the Divine (2002)
No full textInspired by diasporic philosophy, conception, and avidity, Anglophone diasporic authors—such as Rabih Alameddine, a prolific Arab American author recognized for his bold yet creative narratives—have foregrounded heterogeneity, post-nationality, and cross-pollination, as approaches to contest essentialist national identifications and reductionist ethnic ideologies. Equally, diaspora literary criticism emphasizes the importance of border crossings and transnational movements, exemplified in diasporic narratives, prompting a re-evaluation of understandings and mindsets. Drawing on this theoretical premise, this article explores themes of traveling identity and transnational belonging, by meticulously analyzing instances from Rabih Alameddine’s I, the Divine (2002). It also unearths personal and cultural dislocation embodied in the protagonist’s disjointed life narrative, the lack of a central plot, and the uncertainty of claiming an irrevocable belief in belonging to a fixed abode. It concludes that the approach of belonging, the novel advocates, aligns with the postmodernist diasporic view, based on revisiting outdated assumptions of cultural identity and welcoming, instead, hybridity and post-ethnicity, which complicates the fixity of home and the pre-givenness of identity
Interconnectedness: A Study of Forrest Gander’s Be With through the Lens of Ecocriticism
No full textForrest Gander is the 2019 Pulitzer Prize Winner in Poetry for his volume Be With. Although such work is normally regarded as a collection of elegies, Gander’s distinctive poetry writing techniques and ecological insights are profoundly manifest in this collection, with intricate words exploring the relationship between nature and culture, language and perception. Through associating the American eco-critic Scott Slovic’s thoughts about ecocriticism, including the strategy of broadening the analyzing texts of ecocriticism, the reasons for ecological crises, and methods to deal with current environmental problems, this paper aims to examine Gander’s central ecological idea of interconnectedness in Be With.
Leadership and Employee Cultural Perceptions: A Study of West African Migrant Nurses
No full textStudies suggest that migrant nurses from the black and minority ethnic (BME) groups working in the United Kingdom have variously reported negative experiences at work. To learn more about the nature of these experiences, this study explored in detail the experiences of a purposive sample of fifteen (N-15) West African migrant nurses working in selected independent nursing homes in the UK. Data collected was managed and analysed using the interpretative phenomenological analysis (IPA) process to focus on the nurses’ in-depth perceptions through lived experiences on interactions and with managers and colleagues from a different cultural background to further interpret and meanings the nurses attach to these interactions and relationships. Findings suggest the nurses’ sensitivity to managerial and leadership styles at their workplaces. These sensitivities were observed in relation to identified four elements of behaviours and processes at work thematically noted as follows: manager-subordinate relations, channels for the communication of authority, management of organisational processes and age-related values. There are indications of high value significances the nurses attached to the power distance and collectivist cultural values which are consistent with their aboriginal value orientations which informed perceptions and interpretations of experiences at work. The findings have important implications for understanding the organisational leadership styles adjustments needed for the effective management of the well-being of migrant workers in destination countries for optimal engagement. Further practical suggestions include ways of supporting migrant nurse employees in adjusting to destination countries cultural practices at work while also recognising and validating their attachments to cherished aspects of their own indigenous cultural values, especially in multicultural sensitive society such as the United Kingdom
Implementation Challenges of Project Based Learning During Crisis Situations: Strategies for Educational Continuity and Quality
No full textThis study investigated the development and implementation of an integrated crisis-responsive Project Based Learning (PBL) framework across diverse educational contexts. Through a mixed-methods sequential explanatory design conducted over 18 months, the research examined adaptive modalities integration, technical infrastructure optimization, professional development ecosystems, and inclusive system design. The study encompassed 48 educational institutions, involving 240 educators, 1,200 students, and 130 educational specialists and administrators across varied socioeconomic contexts. The investigation employed a three-phase concurrent triangulation approach, utilizing both quantitative and qualitative data collection methods. Quantitative analysis incorporated structural equation modeling and multiple regression analysis, while qualitative data underwent thematic content analysis and cross-case examination. Findings revealed significant correlations between framework integration and learning outcomes (r = 0.78, p < 0.001), demonstrating successful adaptation patterns across varying resource levels. Results indicated that the integrated framework enhanced teaching and learning effectiveness during crisis situations, with 87% of participating institutions reporting improved student engagement and 82% demonstrating enhanced learning outcomes. The technical infrastructure optimization strategies resulted in a 45% reduction in resource utilization while maintaining delivery quality. Professional development initiatives showed a 76% improvement in teacher competency scores, while the inclusive design elements achieved a 92% accessibility rating across diverse learner populations. The study contributes to educational innovation literature by establishing an evidence-based framework for crisis-responsive PBL implementation. Practical implications include structured guidelines for educational institutions implementing adaptive learning systems, resource optimization strategies, and professional development protocols. This research provides valuable insights for educational stakeholders seeking to implement resilient and inclusive PBL systems during crisis situations
The Jeepney Culture of Politeness: A Structural Functionalist Perspective
No full textThe jeepney, tagged as ‘king of the road’ is an intriguing exhibit of Filipino culture. The study aims to explain the systems of action within the jeepney context. Through the lens of Parsons\u27 systemic functionalist perspective, the study examines the driver-passenger interactions to understand how politeness (Lakoff, 1975) is negotiated inside the jeepney. Three specific research questions guided this investigation. First, it explored the linguistic choices Filipino jeepney riders employ to fulfill two main functions of their utterances: paying the fare and getting on/off the jeepney. Second, it examined the mechanisms riders use to negotiate politeness within the jeepney. Finally, it analyzed what these linguistic choices reveal about the cultural practices of the riders. Employing a qualitative-complete observation method, the study\u27s findings reveal a distinct culture of politeness unique to the jeepney environment
The Acceptability Level of Rabbit Meat as an Alternative Meat used in Kapampangan Dishes
No full textThis research aims to look at the acceptability level among the respondents on Rabbit meat as an alternative meat for processed and meat products such as Tocino, Sisig, and Morcon and its implication to the Market Success of Products among selected Kapampangan respondents in the City of San Fernando, Pampanga Specifically, this research would like to identify the following:1.) Demographic profile of the respondents; 2.) Level of acceptability of the respondents on rabbit processed meat in terms of sensory characteristics, price, nutritional value, and culture; and 3.) Relationship of the respondent’s demographic profile and its level of acceptability of rabbit meat as an alternative meat source. In order to see the general picture, a total of 65 researcher-made survey questionnaires were used. The first part was the demographic profile of the respondents, and the second part was their perception of the acceptability of rabbit meat as an alternative source for consumption. The study also used simple random sampling in determining the samples of the study. Kampampangan residents were the respondents to the study. Based on the results, the sensory characteristics of rabbit meat, price, and nutritional value in terms of Tocino, Sisig, and Morcon are acceptable to respondents; thus, there is a great potential to commercialize rabbit meat in the area, although culture-resulting indicators are slightly and somehow some were not acceptable. Pearson’s r also covered a significant relationship in Age, Gender, and Educational Level towards the acceptance of the consumers since the p-value is greater than 0.05. This implies that based on the factors mentioned—Sensory, Price, Nutritional Value, and Culture, Rabbit meat is accepted as Tocino, Sisig, and Morcon servings by the general consumers which can be considered an alternative meat for the aforementioned local cuisine or as an alternative source of different nutritional values. It is recommended that breeders of rabbit meat should continuously educate the public on rabbit meat consumption through advertisements on social media focusing on the different ways to cook rabbit meat in different cuisines. To counter the declining consumption of this valuable meat, reassuring discourses are required to point out its historical merit in health and culture. Also, its distinctive sensorial traits, nutritional profile, and technological properties should be valorized. The need for consumer information, especially on the health benefits of rabbit meat, is crucial to eradicating negative notions about eating rabbit meat
Assessing the Effectiveness of Machine Learning Models in Predicting Stock Price Movements During Energy Crisis: Insights from Shell\u27s Market Dynamics
No full textThe global energy crisis has presented an unprecedented degree of volatility and uncertainty in financial markets, specifically impacting the stock prices of energy sector organizations. Accurate forecasting of stock price patterns during such turbulent periods is essential for informed decision-making by investors, policymakers, and industry stakeholders. The main purpose of this study was to assess the effectiveness of different machine learning models in predicting stock price movements during an energy crisis. This research investigated the stock price fluctuations of Shell during the energy crisis, considering historical data and machine learning techniques to identify patterns and trends. The dataset for this study was sourced from accredited and credible sources providing a more detailed view of how the variation in stock prices of major energy firms was influenced by different energy crises that occurred during the period 2021-2024. The proposed three big energy companies listed under their abbreviations for convenience comprise ExxonMobil (XOM), Shelll-SHEL, and BP-BP; after which historical data was gathered using y-finance. This dataset was of great help to analysts interested in financial analysis, market behavior, and the impact of global events on the energy sector. The data consisted of the daily adjusted closing prices of the selected companies from January 2021 to date. Models like Logistic Regression, Random Forest classifiers, and Support Vector Machines classifiers were deployed since they offered distinct strengths and were capable of offering the right potential. The proven performance metric used encompassed Precision, Recall, Accuracy, and F1-Score. The Random Forest model has the highest accuracy at 0.52, followed by Logistic Regression with an accuracy of 0.51, and then the Support Vector Classifier with an accuracy of 0.50. There are great opportunities in the integration of machine learning and financial forecasting to improve predictive accuracy, especially in these volatile markets where prices fluctuate rapidly with immense uncertainty. Predictive models might also be put into practice by decision-makers within several finance-related spheres to great avail. In this regard, investment firms could practice machine learning for portfolio management by way of automated trading based on market signals in real-time. Predictive modeling of the energy crisis brings huge dividends for investors and analysts. The first among the main recommendations is to take advantage of the model predictions within a diversified investment strategy. In this direction, the investors must use the output of those different predictive models not as an isolated lead but as a complementary tool enhancing the traditional analysis techniques
Integrating Machine Learning and Deep Learning Techniques for Advanced Alzheimer’s Disease Detection through Gait Analysis
No full textAlzheimer\u27s Disease (AD) is a progressive neurodegenerative disorder that severely affects cognitive and motor functions, necessitating early detection for timely intervention and improved patient outcomes. Subtle changes in gait, including stride length and cadence, have been identified as potential early indicators of cognitive decline associated with AD (Del Din et al., 2019). This study leverages advanced deep learning methodologies to enhance the diagnostic capability of gait analysis. Using datasets collected from wearable sensors and motion capture systems, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) were implemented to classify individuals as healthy or at risk for AD. Evaluation metrics, including accuracy, precision, and recall, demonstrated superior performance of deep learning models compared to traditional diagnostic approaches, achieving over 90% classification accuracy in detecting early-stage AD (Esser et al., 2021). These results highlight the transformative potential of AI in healthcare, particularly in non-invasive diagnostic tools for neurodegenerative diseases